1 April 2009 Novel hybrid classified vector quantization using discrete cosine transform for image compression
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Abstract
We present a novel image compression technique using a classified vector Quantizer and singular value decomposition for the efficient representation of still images. The proposed method is called hybrid classified vector quantization. It involves a simple but efficient classifier-based gradient method in the spatial domain, which employs only one threshold to determine the class of the input image block, and uses three AC coefficients of discrete cosine transform coefficients to determine the orientation of the block without employing any threshold. The proposed technique is benchmarked with each of the standard vector quantizers generated using the k-means algorithm, standard classified vector quantizer schemes, and JPEG-2000. Simulation results indicate that the proposed approach alleviates edge degradation and can reconstruct good visual quality images with higher peak signal-to-noise ratio than the benchmarked techniques, or be competitive with them.
© (2009) Society of Photo-Optical Instrumentation Engineers (SPIE)
Ali Hassan Nasser Al-Fayadh, Ali Hassan Nasser Al-Fayadh, Abir Jaafar Hussain, Abir Jaafar Hussain, Paulo Lisboa, Paulo Lisboa, Dhiya Al-Jumeily, Dhiya Al-Jumeily, } "Novel hybrid classified vector quantization using discrete cosine transform for image compression," Journal of Electronic Imaging 18(2), 023003 (1 April 2009). https://doi.org/10.1117/1.3116564 . Submission:
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